hw1: trying t1 and t2

.gitignore

@@ -1,3 +1,5 @@
+ml-venv/
+
 .idea
 *.DS_Store*
 

assignment_1/src/t1.py

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+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from tensorflow.keras import Input, Model
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras import losses
+from tensorflow import keras
+import tensorflow as tf
+
+data = np.load("../data/data.npz")
+xs = data["x"] # 2000x2
+y = data["y"] # 2000x1
+points = 2000
+
+# We manually include in the feature vectors a '1' column corresponding to theta_0,
+# so disable
+lr = LinearRegression(fit_intercept=False)
+
+# Build x feature vector with columns for theta_3 and theta_4
+# variable name explained here: https://vimeo.com/380021022
+X = np.zeros([points, 5])
+X[:, 0] = 1
+X[:, 1:3] = xs
+X[:, 3] = xs[:, 0] * xs[:, 1]
+X[:, 4] = np.sin(xs[:, 0])
+
+# Shuffle our data for division in training, and test set
+np.random.seed(0) # seed the generation for reproducibility purposes
+
+train_ratio = 0.1
+validation_ratio = 0.1
+
+X_t, X_test, y_t, y_test = train_test_split(X, y, test_size=train_ratio)
+X_train, X_val, y_train, y_val = train_test_split(X_t, y_t, test_size=validation_ratio)
+
+# Fit with train data
+reg = lr.fit(X_t, y_t)
+
+print("# Linear regression:")
+
+# Print the resulting parameters
+print("f(x) = %g + %g * x_1 + %g * x_2 + %g * x_1 * x_2 + %g * sin(x_1)" % tuple(reg.coef_))
+
+# Test using MSQ on test set
+score = reg.score(X_test, y_test)
+print("MSQ error on test set is: %g" % (score))
+
+### Non-linear regression:
+
+print("\n# Feed-forward NN:")
+
+A = X_val
+
+X_train = X_train[:, 1:]
+X_val = X_val[:, 1:]
+
+# X_train = X_train[:, 1:3]
+# X_val = X_val[:, 1:3]
+
+mean = np.mean(X_train, axis=0)
+std = np.std(X_train, axis=0)
+
+X_train -= mean
+X_train /= std
+
+X_val -= mean
+X_val /= std
+
+network = Sequential()
+network.add(Dense(100, activation='relu'))
+network.add(Dense(100, activation='relu'))
+network.add(Dense(1, activation='sigmoid'))
+network.compile(optimizer='rmsprop', loss='mse', metrics=['mse'])
+
+network.fit(X_train, y_train, epochs=2000, verbose=1, batch_size=1000, validation_data=(X_val, y_val))
+
+X_val = A
+msq = np.mean((network.predict((X_val[:, 1:] - mean) / std) - y_val) ** 2)
+print(msq)
+
+msq = np.mean((network.predict((X_test[:, 1:] - mean) / std) - y_test) ** 2)
+print(msq)